# model settings
model = dict(
    type='R3Det',
    pretrained='torchvision://resnet50',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_input',
        num_outs=5),
    bbox_head=dict(
        type='RRetinaHead',
        num_classes=1,
        in_channels=256,
        stacked_convs=4,
        use_h_gt=True,
        feat_channels=256,
        anchor_generator=dict(
            type='RAnchorGenerator',
            octave_base_scale=4,
            scales_per_octave=3,
            ratios=[1.0, 0.5, 2.0, 1.0 / 3.0, 3.0, 0.2, 5.0],
            angles=None,
            strides=[8, 16, 32, 64, 128]),
        bbox_coder=dict(
            type='DeltaXYWHABBoxCoder',
            target_means=(.0, .0, .0, .0, .0),
            target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(
            type='SmoothL1Loss',
            beta=0.11,
            loss_weight=1.0)),
    frm_cfgs=[
        dict(
            in_channels=256,
            featmap_strides=[8, 16, 32, 64, 128]),
        dict(
            in_channels=256,
            featmap_strides=[8, 16, 32, 64, 128])
    ],
    num_refine_stages=2,
    refine_heads=[
        dict(
            type='RRetinaRefineHead',
            num_classes=1,
            in_channels=256,
            stacked_convs=4,
            feat_channels=256,
            anchor_generator=dict(
                type='PseudoAnchorGenerator',
                strides=[8, 16, 32, 64, 128]),
            bbox_coder=dict(
                type='DeltaXYWHABBoxCoder',
                target_means=(.0, .0, .0, .0, .0),
                target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
            loss_cls=dict(
                type='FocalLoss',
                use_sigmoid=True,
                gamma=2.0,
                alpha=0.25,
                loss_weight=1.0),
            loss_bbox=dict(
                type='SmoothL1Loss',
                beta=0.11,
                loss_weight=1.0)),
        dict(
            type='RRetinaRefineHead',
            num_classes=1,
            in_channels=256,
            stacked_convs=4,
            feat_channels=256,
            anchor_generator=dict(
                type='PseudoAnchorGenerator',
                strides=[8, 16, 32, 64, 128]),
            bbox_coder=dict(
                type='DeltaXYWHABBoxCoder',
                target_means=(.0, .0, .0, .0, .0),
                target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
            loss_cls=dict(
                type='FocalLoss',
                use_sigmoid=True,
                gamma=2.0,
                alpha=0.25,
                loss_weight=1.0),
            loss_bbox=dict(
                type='SmoothL1Loss',
                beta=0.11,
                loss_weight=1.0)),
    ]
)
# training and testing settings
train_cfg = dict(
    s0=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.3,
            neg_iou_thr=0.2,
            min_pos_iou=0,
            ignore_iof_thr=-1,
            iou_calculator=dict(type='RBboxOverlaps2D')),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    sr=[
        dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.4,
                neg_iou_thr=0.3,
                min_pos_iou=0,
                ignore_iof_thr=-1,
                iou_calculator=dict(type='RBboxOverlaps2D')),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.4,
                min_pos_iou=0,
                ignore_iof_thr=-1,
                iou_calculator=dict(type='RBboxOverlaps2D')),
            allowed_border=-1,
            pos_weight=-1,
            debug=False
        )
    ],
    stage_loss_weights=[1.0, 1.0]
)


test_cfg = dict(
    nms_pre=1000,
    score_thr=0.05,
    nms=dict(type='rnms', iou_thr=0.05),
    max_per_img=100
)

# dataset settings
dataset_type = 'SSDDDataset'
data_root = '../_DATASET/HRSID_s/'
#先计算均值方差
# img_norm_cfg = dict(
#     mean=[17.86386015, 17.86386015 ,17.86386015], std=[14.14670602, 14.14670602, 14.14670602], to_rgb=True)
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='RResize', img_scale=(800,800)),
    dict(type='RRandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_bboxes_ignore']),
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(800,800),
        flip=False,
        transforms=[
            dict(type='RResize', img_scale=(800,800)),
            dict(type='RRandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]

data = dict(
    imgs_per_gpu=6,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'Train/train.txt',
        img_prefix=data_root + 'Train/',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'Train/test.txt',
        img_prefix=data_root + 'Test/',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'Train/test.txt',
        img_prefix=data_root + 'Test/',
        pipeline=test_pipeline))
evaluation = dict(interval=4, metric='mAP')
# evaluation = dict(
#     gt_dir='../_DATASET/SSDD_s/Test/Annotations/',
#     imagesetfile='../_DATASET/SSDD_s/Test/test.txt')
# optimizer

optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))

# optimizer = dict(
#     type='Adam',
#     lr=0.000025)
# optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))

# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=100,
    warmup_ratio=1.0 / 3,
    step=[20, 30])
checkpoint_config = dict(interval=12)
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
    ])
# runtime settings
total_epochs = 36
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
